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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

The Feature Extraction Method of EEG Signals Based on the Loop Coefficient of Transition Network

verfasst von : Mingmin Liu, Qingfang Meng, Qiang Zhang, Hanyong Zhang, Dong Wang

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

High accuracy of epilepsy EEG automatic detection has important clinical research significance. The combination of nonlinear time series analysis and complex network theory made it possible to analyze time series by the statistical characteristics of complex network. In this paper, based on the transition network the feature extraction method of EEG signals was proposed. Based on the complex network, the epileptic EEG data were transformed into the transition network, and the loop coefficient was extracted as the feature to classify the epileptic EEG signals. Experimental results show that the single feature classification based on the extracted feature obtains classification accuracy up to 98.5%, which indicates that the classification accuracy of the single feature based on the transition network was very high.

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Metadaten
Titel
The Feature Extraction Method of EEG Signals Based on the Loop Coefficient of Transition Network
verfasst von
Mingmin Liu
Qingfang Meng
Qiang Zhang
Hanyong Zhang
Dong Wang
Copyright-Jahr
2017
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-63311-4

Electronic ISBN: 978-3-319-63312-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-63312-1_63